Parametrized SQL - Pass column names as a parameter?

[StephanKnox]

Hi all, 

Is there a way to pass a column name(not a value) in a parametrized Spark SQL query?

I am trying to do it like so, however it does not work as I think column name get expanded like 'value' i.e. surrounded by single quotes:

def count_nulls(df:DataFrame, column:str) -> DataFrame:

        return spark.sql("""

                     SELECT count_if({column} IS NULL)

                     FROM {df}

                     """, df=df, column=column)

Does not work, code below returns the correct count:

(spark.sql("""

                     SELECT count_if(city IS NULL)

                     FROM {df}

                     """, df=df)).show()

Than you in advance!

Test data:

Spoiler

from pyspark.sql.types import StructType, StructField, StringType, IntegerType, FloatType, DateType

from pyspark.sql import DataFrame, functions as F

test_data_1 = [

    ('2023-01-10', 123, 50.0, 'bikes', 'LA', {"street_name":"Left Street", "street_number": 1, "zip_code": "8070"}),

    ('2023-01-31', 123, 150.0, None, 'LA', {"street_name":"North Street", "street_number": 1, "zip_code": "1234"}),

    ('2023-01-10', 321, 500.0, 'pans', 'NY', {"street_name":"Dark Street", "street_number": 2, "zip_code": "1234"}),

    ('2023-01-10', 321, 500.0, 'pans', 'NY', {"street_name":"Dark Street", "street_number": 2, "zip_code": "1234"}),

    ('2023-01-10', 123, 5000.0, 'cars', 'LA', {"street_name":"", "street_number": None, "zip_code": ""}),

    ('2023-02-28', 213, 300.0, 'spoons', None, {"street_name":"", "street_number": None, "zip_code": ""}),

    ('2023-03-10', 321, 50000.0, 'cars', 'NY', {"street_name":"", "street_number": None, "zip_code": ""}),

    ('2023-03-31', 213, None, 'cars', 'SF', {"street_name":"", "street_number": None, "zip_code": ""}),

    ('2023-04-30', 432, None, 'plates', 'SF', {"street_name":"", "street_number": None, "zip_code": ""})

]

test_data_schema_1 = StructType([

    StructField("purchase_date", StringType(), True),

    StructField("customer_id", IntegerType(), True),

    StructField("amount", FloatType(), True),

    StructField("category", StringType(), True),

    StructField("city", StringType(), True),

    StructField("address", StructType([

        StructField("street_name", StringType(), True),

        StructField("street_number", IntegerType(), True),

        StructField("zip_code", StringType(), True)

    ]), True)

])

df = spark.createDataFrame(test_data_1, test_data_schema_1)